R. Herkert. Software, (2024)Related to: R. Herkert, P. Buchfink, T. Wenzel, B. Haasdonk, P. Toktaliev, O. Iliev (2024), "Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data". arXiv: 2405.19170.
M. Schmitt. Software, (2023)Related to: Schmitt, M., Radev, S. T., Bürkner, P.-C. (2023). Meta-Uncertainty in Bayesian Model Comparison. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:11-29, 2023.
R. Herkert. Software, (2023)Related to: R. Herkert, P. Buchfink, B. Haasdonk, J. Rettberg, J. Fehr: Randomized Symplectic Model Order Reduction for Hamiltonian Systemsm 2023. arXiv: 2303.04036.
P. Reiser, J. Aguilar, A. Guthke, and P. Bürkner. Software, (2024)Related to: Reiser P., Aguilar J. E., Guthke A., & Bürkner P. C. (2023). Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference. ArXiv preprint 2312.05153. arXiv: 2312.05153.
M. Kelm, C. Bringedal, and B. Flemisch. Dataset, (2023)Related to: Kelm, M., Gärttner, S., Bringedal, C. et al. Comparison study of phase-field and level-set method for three-phase systems including two minerals. Comput Geosci 26, 545-570 (2022). doi: 10.1007/s10596-022-10142-w.
G. Tkachev. Software, (2021)Related to: G. Tkachev, S. Frey and T. Ertl, "S4: Self-Supervised learning of Spatiotemporal Similarity," in IEEE Transactions on Visualization and Computer Graphics. doi: 10.1109/TVCG.2021.3101418.
P. Santana Chacon, M. Hammer, I. Wochner, J. Walter, and S. Schmitt. Software, (2023)Related to: P. F. S. Chacon, M. Hammer, I. Wochner, J. R. Walter and S. Schmitt. A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers. doi: 10.1080/10255842.2023.2293652.
H. Jäger. Software, (2023)Related to: Jäger, Henrik, Alexander Schlaich, Jie Yang, Cheng Lian, Svyatoslav Kondrat und Christian Holm. 2023. A screening of results on the decay length in concentrated electrolytes. Faraday Discussions. Faraday Discussions (Februar). doi: 10.1039/d3fd00043e.
A. Schlaich. Software, (2024)Related to: Alexander Schlaich, Matthieu Vandamme, Marie Plazanet, Benoit Coasne, "Bridging Microscopic Dynamics and Hydraulic Permeability in Mechanically-Deformed Nanoporous Materials", (2024). arXiv: arXiv:2403.19812.
J. Magiera. Dataset, (2024)Related to: Jim Magiera, Deep Ray, Jan S. Hesthaven, Christian Rohde, Constraint-aware neural networks for Riemann problems, Journal of Computational Physics, Volume 409, 2020, 109345. doi: 10.1016/j.jcp.2020.109345.
C. Homs Pons, and R. Lautenschlager. Software, (2024)Related to: Coupled Simulations and Parameter Inversion for Neural System and Electrophysiological Muscle Models, submitted to GAMM Mitteilungen.
S. Tovey, C. Lohrmann, and C. Holm. Dataset, (2024)Related to: Tovey, Samuel James and Lohrmann, Christoph and Holm, Christian, Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning, Machine Learning: Science and Technology, 2024. doi: 10.1088/2632-2153/ad5f73.
D. Holzmüller. Software, (2022)Related to: David Holzmüller and Dirk Pflüger. Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. Sparse Grids and Applications - Munich 2018 (2021). doi: 10.1007/978-3-030-81362-8_4.
J. Finkbeiner, S. Tovey, and C. Holm. Dataset, (2024)Related to: Jan Finkbeiner, Samuel Tovey, Christian Holm: Generating Minimal Training Sets for Machine Learned Potentials (2023). arXiv: 2309.03840.
T. Praditia, M. Karlbauer, S. Otte, S. Oladyshkin, M. Butz, and W. Nowak. Dataset, (2022)Related to: Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S., Butz, M.V., Nowak, W.: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network. Earth and Space Science Open Archive (2022). doi: 10.1002/essoar.10511934.1.
M. Alvarez Chaves, H. Gupta, U. Ehret, and A. Guthke. Software, (2024)Related to: Álvarez Chaves, Manuel, Gupta, Hoshin V., Ehret, Uwe and Guthke, Anneli. On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data. Entropy 2024, 26(5), 387. doi: 10.3390/e26050387.
D. Holzmüller. Software, (2021)Related to: David Holzmüller. On the Universality of the Double Descent Peak in Ridgeless Regression. International Conference on Learning Representations, 2021. arXiv: 2010.01851.
I. Banerjee. Dataset, (2021)Related to: Banerjee, I., Guthke, A., Van de Ven, C. J. C., Mumford, K. G. & Nowak, W. (2021). Overcoming the Model-Data-Fit Problem in Porous Media: A Quantitative Method to Compare Invasion-Percolation Models to High-Resolution Data. Water Resources Research, 57, e2021WR029986. doi: 10.1029/2021WR029986.